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Token 1.10: Large vs Small in AI: The Language Model Size Dilemma

Your Guide to the Pros and Cons of Large and Small AI Models


Today’s Token looks at a big question in AI: should we keep making larger language models (LLMs) or switch to smaller, more efficient ones? We'll explore what each choice means for AI's future.

LLMs are impressive, handling complex tasks well. Recently released Falcon has 180 billion parameters and is trained on 3.5 trillion tokens! But large models need a lot of power and aren't easy to use everywhere.

Small Language Models (SLMs), with 10 billion parameters or less, can do similar jobs but don't require as much power. They're easier to manage and cost less. A central theme is model compression, essential for converting large models into efficient, smaller versions. Techniques like pruning, quantization, and knowledge distillation are crucial, especially for resource-limited applications.

Finally, we’ll talk about starting with small models from the beginning. This means using less data and focusing on specific tasks, which can be a smarter way to build AI. This article will discuss the pros and cons of both big and small AI models, helping you understand which might be best for different uses. Let’s dive in:

  • Comparison

  • Solution – Model Compression (for LLMs):

    • Pruning

    • Quantization

    • Knowledge distillation

    • Low-rank factorization for model compression

  • Don’t compress – start small

  • List of Small Models

  • Conclusion


LLMs are truly powerful in language processing but have practical challenges due to their large size and high computational requirements. These challenges become especially significant in environments with limited resources. The main costs affecting their development and use include:

  • Computational resources: Training requires significant processing power, often needing GPUs or TPUs, which leads to high energy costs for prolonged training periods.

  • Storage: These models and their datasets need a lot of storage space, requiring high-capacity and fast-access storage systems.

  • Maintenance and updates: They need ongoing maintenance, including regular monitoring, updating for accuracy and relevance, and periodic retraining.

  • Infrastructure and support: Operational use requires robust infrastructure like servers and networks, along with support systems for deployment and monitoring.

  • Energy consumption and environmental impact: Their high energy use, particularly during training, leads to higher costs and potential environmental impacts. Efforts to use renewable energy sources also contribute to costs.

Smaller foundation models offer several benefits:

  • Affordability and accessibility: They are less expensive and require fewer resources, making them more accessible to individuals and small teams.

  • Adaptability and innovation: These models are easier to modify and fine-tune, allowing for the integration of specific data and adjustments to meet unique needs, promoting innovation.

  • Data governance and privacy: They can operate on local systems without needing high-end GPUs, giving users more control over their data and enhancing privacy.

  • Faster development and testing: Their smaller size and simplicity support quick prototyping and experimentation.

Solution – Model Compression

As big models face more problems, model compression, the study of making them smaller and more efficient, is gaining importance. To understand how large models can be transformed, let’s examine specific characteristics of the model that can be adjusted or reduced while preserving performance.

These key metrics include:

  • Number of parameters: Total count of learnable weights in a model. More parameters generally mean greater expressiveness, but also higher demands for computational resources and memory during training and inference.

  • Model size: The disk space or memory needed to store the entire model, including weights, biases, and other components. This size is influenced by the number of parameters, the data type of parameters, and the model architecture.

  • Compression ratio: The ratio between the size of the original model and a compressed model with retained performance. A higher ratio means more efficient compression.

  • Inference time: How long the model takes to process input data and generate responses.

  • Floating Point Operations (FLOPs): A number of arithmetic operations involving floating-point numbers performed by the model during data processing. FLOPs help estimate a model’s computational requirements and compare the efficiency of different models or compression techniques.

In the following exploration of model compression techniques, we want to focus on large language models (LLMs) as the most applicable type of foundation models (FMs).

Model compression for LLMs

A recent paper “A Survey on Model Compression for Large Language Models” presents a very extensive analysis of model compression techniques up to date. 

Source: ‘A Survey on Model Compression for Large Language Models’

Each of the named techniques is a separate area of research. What are they? →

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As we've navigated the complexities of LLMs and small language models SLMs, one thing is clear: the future of AI isn't just about size, but about smart, efficient choices. Yes, annual hardware advancements could further accelerate the growth of large models, revealing yet unknown properties of LLMs. But optimization methods are making models more efficient without hitting diminishing returns in LLMs scaling. Whether you're captivated by the vast capabilities of LLMs or the agility and resourcefulness of SLMs, understanding the nuances of each is crucial for harnessing their full potential.

Our deep dive into model compression techniques for LLMs and the strategic approach of starting with smaller models offers a roadmap for those looking to optimize AI for various applications. From the intricate processes of pruning and quantization to the innovative realms of knowledge distillation and low-rank factorization, we've explored paths to make large models more manageable and small models more powerful.

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Previously in the FM/LLM series:

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